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  • View in gallery

    Time mean horizontal distributions of the net heat flux (W m−2) into the ocean for (a) the first-year mean simulation from D2, and (b) the ECMWF+UMD data averaged over the year 1989

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    Horizontal distributions of simulated SST (°C) of (a) D2 and (b) the NCEP SST averaged for the period of Nov 1992–Feb 1993

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    Time evolution of zonal distributions of net surface heat flux into the ocean along the equator from 1989 to 1993 (a) in D2 and (b) from ECMWF+UMD data. Contour interval is 25 W m−2 with areas of negative and large positive values (>125 W m−2) shaded

  • View in gallery

    Time–zonal distributions of SST along the equator for (a) simulation D2 and (b) the NCEP data. Contour interval is 0.5°C with areas of SST > 29° and SST < 25°C shaded

  • View in gallery

    Time series of SST (°C) average within (a) 150°–170°E and (b) 100°–120°W along the equator simulated in the four experiments and observed values (NCEP data)

  • View in gallery

    Time series of (a) surface net heat flux (W m−2) into the ocean and (b) solar radiative flux (W m−2) during TOGA COARE (Nov 1992–Feb 1993) period. The solid line denotes D2 and the dashed line presents the observation at IMET buoy

  • View in gallery

    Time series of (a) SST (°C) and (b) mixed layer depth (m) during TOGA COARE (Nov 1992–Feb 1993) period. The solid line denotes D2 and the dashed line presents the observation at IMET buoy

  • View in gallery

    Time mean xz cross section of (a) temperature, (b) salinity, and (c) zonal current, along the equator during 1992–93 in D2. Contour intervals are 1°C, 0.1 psu, and 0.1 m s−1, respectively, with negative zonal current shaded

  • View in gallery

    As in Fig. 8 except for D2−M2. Contour intervals are 0.2°C, 0.1 psu, and 0.05 m s−1, respectively, with negative values shaded

  • View in gallery

    Separation of the total heat budgets into daily anomalies, seasonal to interannual anomalies, and time mean component within each 5° lat and 20° lon box along the equator during 1992–93 in D2

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    Time series of (a) mixed layer heat budgets for M2 and (b),(c) cumulative difference for D2−M2 averaged in the box (2.5°S–2.5°N, 150°–170°E). Unit is °C day−1

  • View in gallery

    As in Fig. 11 except averaged in the box (2.5°S–2.5°N, 100°–120°W)

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The Role of Daily Surface Forcing in the Upper Ocean over the Tropical Pacific: A Numerical Study

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  • 1 NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 2 NOAA/NESDIS/Office of Research and Applications, Camp Springs, Maryland
  • | 3 NASA Goddard Space Flight Center, Greenbelt, Maryland
  • | 4 NOAA/NESDIS/Office of Research and Applications, Camp Springs, Maryland
  • | 5 Department of Meteorology, University of Maryland at College Park, College Park, Maryland
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Abstract

The impacts of high-frequency surface forcing in the upper ocean over the equatorial Pacific are investigated using a nonlinear reduced-gravity isopycnal ocean circulation model forced by daily and monthly mean forcing. The simulated sea surface temperature (SST) in the daily forcing experiment is colder than that in the monthly forcing experiment near the equator. A mixed layer heat budget calculation shows that the net surface heat flux is primarily responsible for the SST difference in the western Pacific, while zonal advection accounts for the SST difference in the eastern Pacific where other budget terms are large but canceling each other. The daily forcing primarily enhances vertical mixing that reduces the vertical shear of the upper ocean. It also changes the net heat into the ocean through two contrasting processes: one is the increased surface latent heat loss induced by transient winds and the other is colder SST due to stronger mixing, which further reduces heat loss at the surface. As a result, the annual mean net surface heat flux into the ocean is reduced and the meridional thermal advection is weaker. The daily forcing also impacts the variation of the thermocline through a changing mixed layer depth so that the temperature in the simulation with the daily forcing is warmer around the thermocline.

Current affiliation: Institute of Hydrological Sciences, National Central University, Junli, Taiwan

Corresponding author address: Dr. Chung-Hsiung Sui, Institute of Hydrological Sciences, National Central University, 300, Junda Rd., Junli City, Taoyan, 320 Taiwan. Email: sui@cc.ncu.edu.tw

Abstract

The impacts of high-frequency surface forcing in the upper ocean over the equatorial Pacific are investigated using a nonlinear reduced-gravity isopycnal ocean circulation model forced by daily and monthly mean forcing. The simulated sea surface temperature (SST) in the daily forcing experiment is colder than that in the monthly forcing experiment near the equator. A mixed layer heat budget calculation shows that the net surface heat flux is primarily responsible for the SST difference in the western Pacific, while zonal advection accounts for the SST difference in the eastern Pacific where other budget terms are large but canceling each other. The daily forcing primarily enhances vertical mixing that reduces the vertical shear of the upper ocean. It also changes the net heat into the ocean through two contrasting processes: one is the increased surface latent heat loss induced by transient winds and the other is colder SST due to stronger mixing, which further reduces heat loss at the surface. As a result, the annual mean net surface heat flux into the ocean is reduced and the meridional thermal advection is weaker. The daily forcing also impacts the variation of the thermocline through a changing mixed layer depth so that the temperature in the simulation with the daily forcing is warmer around the thermocline.

Current affiliation: Institute of Hydrological Sciences, National Central University, Junli, Taiwan

Corresponding author address: Dr. Chung-Hsiung Sui, Institute of Hydrological Sciences, National Central University, 300, Junda Rd., Junli City, Taoyan, 320 Taiwan. Email: sui@cc.ncu.edu.tw

1. Introduction

The El Niño–Southern Oscillation (ENSO) over the tropical Pacific influences global climate. Observational and modeling studies in recent decades firmly established that air–sea interaction is a crucial process in ENSO evolution (e.g., Rasmusson and Carpenter 1982; Neelin et al. 1998). In most studies, monthly mean surface fluxes are analyzed or prescribed as forcing to ocean models due to the lack of daily data although high-frequency wind forcing has been used recently (e.g., Borovikov et al. 2001). It is less common to use high-frequency surface heat (in particular radiation) and freshwater fluxes since the data are not very reliable. It is true that ocean–atmosphere climate models typically couple daily. But the treatment of high-frequency air–sea interactive processes in climate models is still far from satisfactory.

Actual high-frequency air–sea interaction observations have been reported in many recent studies. In the western tropical Pacific, observational studies for the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) show that the sea surface temperature (SST) has strong diurnal variations with amplitudes of 1°–2°C modulated by intraseasonal variability (e.g., Weller and Anderson 1996; Lau and Sui 1997; Sui et al. 1997). Sui et al. (1997) showed that the diurnal SST variability is primarily determined by the absorbed solar radiation near the surface (∼1 m) and that the intraseasonal variability is determined by the solar radiation penetrating farther below. They also found that the asymmetric diurnal mixing cycles are essential for maintaining stable temperature stratification and realistic evolution of SST at the intraseasonal timescale. Analyses of intraseasonal variability of SST and surface fluxes (e.g., Krishnamurti et al. 1988; Zhang and McPhaden 1995; Hendon and Glick 1997; Shinoda et al. 1998) show that equatorial oceans have distinct signals in the surface fluxes of momentum, heat, and moisture. In the eastern tropical Pacific, recent studies by Chelton et al. (2001) and Thum et al. (2002) showed clear signals of the atmospheric boundary layer coupling to oceanic tropical instability waves with periods of 20–40 days and wavelengths of 1000–2000 km.

The effects of high-frequency (daily to intraseasonal) surface forcing in climate variability have been debated recently. Kirtman and Schopf (1998) used a simple coupled model to examine decadal variations in ENSO prediction skill and predictability. They found that inclusion of atmospheric “weather noise” leads to an irregularity of ENSO events, associated with a change of the dominant period and amplitude on a decadal scale, indicating the important role of high-frequency surface forcing in the evolution of climate variability. Wang et al. (1999) used a stochastically forced nonlinear dynamic model to study the nature of the highly irregular ENSO cycle, and found that the intraseasonal noise can alter the dominant period of the intrinsic nonlinear oscillation. The onset of the most recent ENSO (1997/98) event coincided with extraordinary Madden–Julian oscillation (MJO) in the winter of 1996/97. The occurrence of the warm event was forecasted some nine months in advance by many dynamic coupled models that do not contain explicit MJO. However, such models cannot forecast the rapidity of onset and the maximum intensity of the warming, implying that MJO plays an important role in ENSO onset. This became the main topic of the workshop on MJO and ENSO held in March 2000 at the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamic Laboratory (GFDL) as reported in Zhang et al. (2001).

Since how the temporal and spatial structures of noise affect the low-frequency variability of the coupled system remains as an issue of controversy, there is no easy way to separate the spatial and temporal structure of high-frequency forcing from that of low-frequency fields. We decide in this study to simply examine the impacts of daily surface forcing in the upper ocean over the equatorial Pacific using a reduced-gravity isopycnal ocean general circulation model. Shinoda and Hendon (2001) adopted a similar approach in their study of the upper-ocean response to the intraseasonal oscillation in the western equatorial Pacific.

In the following discussion, the model is briefly described in section 2 together with a discussion of surface forcing and experiment design. In section 3, the model net surface heat fluxes and the corresponding SST and upper-ocean structure are compared with that of the observed. In section 4, heat budgets of the surface layer are calculated to offer some insights of physical processes responsible for the impacts of high-frequency forcing in the upper ocean. A summary is given in section 5.

2. Model, surface forcing, and experiment design

The ocean model used in this study was developed by Schopf and Loughe (1995). The updated version of the ocean model includes salinity (Yang et al. 1999). The model domain is confined within the Pacific Ocean (45°S–65°N, 120°E–70°W) in this study with realistic boundaries. The model uses a latitude–longitude horizontal grid and a generalized vertical coordinate. The horizontal resolution is 1° in longitude and stretched in the meridional direction varying from 1/3° within the equatorial zone (10°S–10°N) to 1° in latitude beyond the equatorial zone. In the vertical, a hydrostatic Boussinesq approximation is applied, and 20 quasi-isopycnal layers are used. A minimum vertical spacing is maintained at 5 m (10 m for the mixed layer), but the layer thicknesses are otherwise allowed to vary. In addition to currents (u, υ), temperature (T), and salinity (S), the layer depth (h) of these isopycnal layers is also a prognostic variable. The uppermost layer is governed by explicit mixed layer physics and is treated as a bulk turbulent well-mixed layer (Niiler and Kraus 1977). The mixed layer is incorporated in the model through the prescription of an entrainment mass flux through the base of the top layer. Mass flux across the surface of the ocean is determined by the difference between precipitation and evaporation. Below the model's surface layers, the vertical mixing scheme of Pacanowski and Philander (1981) is used. In the horizontal, an eighth-order Shapiro (1970) filter is applied to momentum and layer thickness, and a fourth-order filter to temperature and salinity.

The model is forced at the surface with momentum, heat, and freshwater fluxes. Four experiments, D1, M1, D2, and M2, are performed with different surface forcing fields and background mixings. The model is forced by daily surface fluxes (momentum, surface solar radiation, sensible and latent heat fluxes) in D1 and D2, whereas it is forced by monthly mean surface fluxes in M1 and M2. Different vertical background diffusion coefficients are used in D2 and M2 (5 × 10−6 m2 s−1) and D1 and M1 (10−5 m2 s−1). We know from other experiments that the model SST and thermocline gradients are dependent on the vertical diffusion, so we did these simulations to test the robustness of the results. The daily surface momentum flux is converted from Special Sensor Microwave Imager (SSM/I) wind data (Atlas et al. 1991, P. 204, 205, 208) using a bulk aerodynamic formula. The surface sensible and latent heat fluxes are calculated using the diagnostic atmospheric boundary layer (ABL) model of Seager et al. (1995) with the time-varying air temperature and specific humidity prescribed at the lateral boundary. The lateral boundary values are from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis and the model's prognostic SST. The longwave radiation is calculated from model SST, atmospheric specific humidity, atmospheric air temperature, and prescribed cloud cover, etc., using an empirical formula. See Seager et al. (1995) for details. The solar radiation is based on climatological solar radiation from the Earth Radiation Budget Experiment (Harrison et al. 1993) and the recently available daily solar radiation budget data produced by Pinker and Laszlo at the University of Maryland (hereafter referred to as UMD data; more information available online at http://metosrv2.umd.edu/nsrb/pathfinder/04ava-data.htm). The UMD data is derived based on the International Satellite Cloud Climatology Project (ISCCP; Schiffer and Rossow 1985; Rossow and Schiffer 1991) data and the Global Energy and Water Cycle Experiment (GEWEX) Surface Radiation Budget (SRB) algorithm (Pinker and Laszlo 1992; Laszlo et al. 1997). All four experiments are subjected to the same monthly mean precipitation rates (Xie and Arkin 1996) from 1989 to 1993. At the southern boundary the model temperature and salinity are relaxed to the Levitus and Boyer (1994) monthly climatology. The model is first spun up for 10 yr under climatological forcing, then is run for additional 5 yr under the surface forcing from 1989 to 1993. The daily output from the 1989–93 simulations are analyzed. For comparison, the monthly optimum interpolation (OI) SST merged ship and satellite observations derived by Reynolds and Smith (1994) are used. The observations at the improved meteorological (IMET) surface mooring buoy (1.75°S, 156°E) during TOGA COARE (Weller and Anderson 1996), and at TOGA Tropical Atmosphere–Ocean (TAO) moorings (McPhaden 1993, 1995) in the western and eastern equatorial Pacific are also used for comparison.

3. Surface fluxes and model responses

We first compare the net heat flux into the ocean between the model and the observed. The observed surface flux is the sum of sensible, latent, and longwave radiative fluxes from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis plus the net solar radiation of the UMD data. The decision to substitute ECMWF net solar radiation with UMD data is based on the fact that shortwave (SW) fluxes are mainly affected by cloudiness, and satellites can observe clouds better than models can generate them. We made a comparison between the ECMWF SW fluxes and the UMD fluxes. The spatial and temporal variability of the latter is significantly better than the former (figure not shown). In addition, the UMD satellite estimates were evaluated against ground truth as reported in Whitlock et al. (1995) and Garatuza-Payan et al. (2001). The UMD SW fluxes were also used to evaluate the fluxes computed from various models, like the National Meteorological Center's (NMC's) global model (Campana et al. 1993, 1994), NCAR Community Climate Model version 2 (CCM2; Ward 1995; Hahmann et al. 1995), and the radiation estimates in the National Aeronautics and Space Administration (NASA) Data Assimilation Office (DAO) and NCEP–NCAR reanalyses by Bony et al. (1997). In these studies, differences between the models' mean SW downward flux and the satellite estimate are generally larger than satellite retrieval errors. Most of the discrepancies are attributed to differences between cloud observations and model-diagnosed clouds. There are also indirect types of evaluation available. For instance, using cloud products from the UMD radiation model, has a positive impact in a regional atmospheric model, as described in Yucel et al (2002).

Figure 1 shows time mean horizontal distributions of net heat flux into the ocean for the first-year simulation of D2 and the corresponding observed heat flux in 1989. Observed and simulated fields in Fig. 1 display similar horizontal patterns in which maximum fluxes (positive downward) reside slightly south of the equator extending from the coast of South America to the western Pacific warm pool. This is a result of large solar radiation in the absence of deep convection, and much less latent and sensible heat loss over the cold tongue. Away from the equator to the intertropical convegence zone (ITCZ) and the South Pacific convergence zone (SPCZ) the surface flux decreases sharply due to the strong albedo effect of clouds. Farther away from the equator in the midlatitude storm tracks, the flux is negative because the warm ocean is in contact with the dry, cold air from the continent, especially in the winter. However, the maximum net flux in D2 is about 25–50 W m−2 smaller than that from the ECMWF+UMD data mostly due to large latent heat flux generated in D2. Consistently, the SST simulated in D2 is colder than the observation as shown by the mean SST distribution for the period of November 1992–February 1993 in Fig. 2 except in the equatorial eastern Pacific where the simulated SST is warmer than the observed. Figure 2 further shows that the simulated warm pool area (SST > 29°C) is significantly smaller than the observed, although simulated and observed SST patterns are similar.

Figure 3 shows the time evolution of net surface heat flux along the equator in D2 and the ECMWF+UMD data from 1989 to 1993. As noted before, the net flux in D2 is about 25–50 W m−2 smaller than that from the ECMWF+UMD data. Otherwise, both display similar time evolution with a strong annual cycle over the eastern equatorial Pacific where the strongest downward flux occurs in the spring. A careful examination of Fig. 3 further reveals that maximum net heat fluxes into the eastern equatorial Pacific in D2 lead that in observed fluxes by about three months. This is likely a feedback effect between the SST and air–sea fluxes calculated by the atmospheric boundary layer model. A weak interannual disturbance is evident in both simulated and observed fluxes during 1991–92 in the central equatorial Pacific. In the western equatorial Pacific, the net heat fluxes in Figs. 3a and 3b show a dominant subseasonal variability. The corresponding model and observed SSTs along the equator are shown in Fig. 4. The figure shows that the maximum SST in D2 is about 2°C warmer than the NCEP data over the eastern equatorial Pacific while SSTs in the western Pacific have similar magnitude. Thus, the simulated zonal SST gradient in the Tropics is weaker than that observed. The model seems to have missed a weak warm event aroung 160°W during the winter of 1992.

The time evolution of SST averaged within 150°–170°E and within 100°–120°W along the equator in the four experiments and the NCEP data are further examined in Fig. 5. First we note that simulated SSTs and the observed SST in the equatorial western Pacific (Fig. 5a) all exhibit a similar semiannual variability so the SST differences between the model and observation are independent of season. This is quite different from SSTs in the eastern equatorial Pacific (Fig. 5b) where simulated SSTs in the four experiments are similar to the observed SST in each spring, and are 1°–2°C warmer than the observed SST in each fall. This indicates that the model annual cycle in the eastern Pacific is too weak, suggesting that the model has a systematic bias that is not directly related to the higher-frequency atmospheric signals or the lack of them. More possible reasons are the systematic errors of the surface wind fields as well as the model vertical mixing parameterization.

Figure 5a further shows that, in the western equatorial Pacific, SSTs simulated in M1 and M2 are warmer than in the observation, whereas those simulated in D1 and D2 are colder than in the observation. This shows the roles played by transient winds in causing latent heat loss, a process that is very important since the annual mean winds there are nearly negligible. Moreover, our experiments also demonstrate the roles of the background mixing process under transient wind forcing. The SSTs simulated in M1 and M2 are quite similar. However, the SST simulated in D1 is about 1°C colder than that simulated in D2. These features are also evident in the springtime in the equatorial eastern Pacific (Fig. 5b). As expected, a larger background vertical diffusion coefficient produces a lower SST. However, the model vertical mixing process is more efficient under more transient surface forcing. Without high-frequency forcing, the model is less sensitive to variations of the vertical diffusion process.

The important role of daily forcing in the mixed layer simulation is further examined by analyzing the time evolution of surface flux and mixed layer properties from D2 and observations during TOGA COARE as shown in Figs. 6 and 7. The daily net surface heat fluxes in the D2 and COARE observations are very close to each other (Fig. 6a) mainly due to similar daily solar radiative fluxes observed from the IMET buoy (Weller and Anderson 1996) and the UMD data (Fig. 6b). It is also suggested that the Seager ABL model, given daily wind forcing, catches the atmospheric heat balance near the surface well, which produces sensible and latent heat components realistically. This produces a similar time evolution of upper-ocean temperature (represented by top 10-m mean temperature) simulated in D2 and observed at the IMET buoy (Fig. 7a). The major model error is that the simulated temperature has weaker amplitude than the observed. This is because the simulated mixed layer is much deeper than the observed mixed layer (Fig. 7b), possibly due to problems in the oceanic surface layer parameterization. In addition to the surface heat fluxes, horizontal advection also contributes to the change of upper-ocean temperature in the western equatorial Pacific as found in the model mixed layer budget (figure not shown). This is in qualitative agreement with observations obtained in TOGA COARE (Cronin and McPhaden 1997).

To examine the overall effect of high-frequency forcing on the tropical upper ocean, we examine the time mean fields of model temperature, salinity, and zonal current in the last two years (1992–93). The mean xz cross sections of the above-mentioned variables along the equator (Fig. 8) and the mean horizontal fields in the mixed layer (figure not shown) reveal the well-known structures of the thermocline, the mixed layer, the south equatorial current, and the equatorial undercurrent. The corresponding difference (D2−M2) fields (Fig. 9) show that the model forced by the daily surface forcing in D2 generates colder temperature in the equatorial Pacific surface layer, but warmer temperature in the thermocline than does the model forced by the monthly mean surface forcing in M2 (Fig. 9a). The model in D2 produces saltier water in the western equatorial Pacific surface layer, but fresher water in the thermocline than does the model in M2 (Fig. 9b). The vertical shear of zonal current in the upper ocean is weaker in D2 than in M2 (Fig. 9c). These differences between the two experiments are caused by stronger mixing in D2, and stronger exchange between the surface layer and thermocline as a result of the daily surface forcing.

4. Mixed layer heat budgets

To identify the processes responsible for the different thermal response of the mixed layer between D2 and M2 discussed above, the heat budget in the surface mixed layer in D2 and M2 are calculated. The heat balance in the mixed layer is expressed as
i1520-0442-16-4-756-e1
where h is the mixed layer depth, V the horizontal current, ζ the generalized vertical coordinate, κ the vertical diffusivity coefficient, Q the heat flux (sensible, latent, and radiative fluxes absorbed in the mixed layer), and we the entrainment velocity at the base of the mixed layer. The first term in the right-hand side of (1) is the horizontal advection term. It consists of zonal and meridional components that are referred to as ZADV (zonal thermal advection) and MADV (meridional thermal advection) below. The second, third, and fourth terms in the right-hand side of (1) are referred to as entrainment (TE), vertical diffusion (VD), and net heat flux (TF), respectively. The SST tendency term on the left-hand side of (1) is referred to as TT.

The seasonal cycle of the mixed layer heat budget in the equatorial Pacific is consistent with the result of Borovikov et al. (2001) who identified the dominant processes governing the seasonal cycle of SST across the basin in the same model as used in this study. They also compared the model heat budgets with other model results and observational analyses. Readers are referred to Borovikov et al. (2001) for a review and discussion of the surface heat balance in the equatorial Pacific Ocean. In the following, we shall focus on the effect of high-frequency forcing on the surface heat balance.

a. A scale separation of the time mean heat budget

To examine the role of high-frequency signals in mixed layer heat balance along the equatorial Pacific, the heat budgets during 1992–93 are calculated for D2. We calculate the heat budget using daily, monthly, and the period mean data. Using H to denote each term of the heat budget, and Hdaily, Hmonthly, and Hmean to denote the corresponding budget calculation using the daily, monthly, and time mean data, respectively, the time mean of daily budgets (total) are then separated into three parts: daily anomalies = HdailyHmonthly, seasonal to interannual anomalies = HmonthlyHmean, and time mean component = Hmean. The breakdown of all terms of the total heat budget within each 5° latitude and 20° longitude box along the equator is shown in Fig. 10. The scale separation of the contributions to the mean heat budget of the mixed layer indicates that higher-frequency signals affect the horizontal heat convergence (MADV and ZADV) near the equator significantly, possibly through the instability waves in the central and eastern Pacific (Legeckis 1977). The high-frequency disturbances also contribute significantly to the dominant change in the VD. The time mean TE, however, dominates the vertical transport. The TF is a linear term, so the effects of the high-frequency heat fluctuation cannot be evaluated directly from it. However, it does not mean its role is not important because it can change the stability and generate eddies. So a more direct evaluation of this effect, like through calculating generation of available potential energy, is required. This is beyond the scope of this study.

b. Difference of heat budgets between daily and monthly experiments

Figure 5 shows that the SST differences between D2 and M2 (or between D1 and M1) are generated within the first year (1989) in which the SST increases significantly in M1 and M2 whereas the SST increases slowly in D1 and D2. To explain the SST difference between D2 and M2 over the western equatorial Pacific, the time evolution of mixed layer heat budget in M2 in the equatorial band (2.5°S–2.5°N) and the cumulated heat budget difference (D2−M2) averaged within 150°–170°E in the first year are plotted in Fig. 11. The TF is the largest term over the western equatorial Pacific, and the semiannual cycle dominates the surface heat flux that is responsible for the semiannual variation of SST as shown in Fig. 5a. The TF is largely balanced by the TE and VD terms, consistent with all other heat balance studies (e.g., Borovikov et al. 2001; Shinoda and Hendon 2001). The cumulated heat budget difference D2−M2 in Fig. 11b reveals that the TF and MADV contribute to the negative SST difference between D2−M2, whereas TE, VD, and ZADV induce a positive SST difference. The yearly cumulative difference by MADV (−1.52°C) is nearly balanced by the yearly cumulative difference of ZADV (0.48°C) and VD (1.03°C; Fig. 11c). Thus the cumulated TT (SST tendency) difference (−0.42°C) is determined by TF (−2.77°C) plus TE (2.36°C). The features of cumulative budget difference discussed above are evident throughout the year (Fig. 11c). This indicates that the high-frequency effect on SST difference in the western Pacific is determined by one-dimensional mixed layer physics, where the advection processes are not important. Thus, larger latent heat loss is responsible for slower increase of SST in D2 than in M2, which is consistent with the results of Rosati and Miyakoda (1988). But the net latent heat loss is accomplished through two contrasting processes as stated below.

Over the eastern equatorial Pacific, the time evolution of the heat budget averaged in the equatorial band 2.5°S–2.5°N within 100°–120°W is shown in Fig. 12a. The figure shows that all terms are important with TF and VD as two leading terms of opposite signs. Both terms show a clear annual cycle with peaks in early April responsible for the SST peak in May as shown in Fig. 5b. The cumulative heat budget difference (D2−M2) is shown in Figs. 12b and 12c. The budget differences reveal that the TF, and MADV and ZADV contribute to the negative SST differences between D2 and M2, whereas TE and VD induce a positive SST difference (Fig. 12b). Unlike the western equatorial Pacific, differences of horizontal advection (ZADV and MADV) are of opposite signs relative to the difference of vertical processes (VD and TE) in the heat budget. In the first half year, the ZADV difference is small; the TT difference is determined by the TF+MADV and the TE+VD differences that are near balance. But cumulated into the second half year, the differences by TF (−7.92°C), MADV (−8.22°C), TE (2.64°C), and VD (−13.72°C) cancel each other so that the cumulated difference by ZADV (−1.37°C) largely accounts for cumulated SST difference (TT, −1.16°C; Fig. 12c). The weaker warm zonal advection in D2 results from a weaker zonal SST gradient due to enhanced mixing in D2 than in M2.

The first-year budget difference discussed above shows that the TF difference between daily and monthly forcing experiments is negative across the equatorial basin. The major cause is attributed to the increased heat (latent plus sensible flux) loss induced by the transient winds. But a further analysis of the SST differences between D2 and M2 and the corresponding net heat flux differences along the equatorial band for the 5-yr period shows a clear negative correlation (figure not shown); that is, a more negative SST difference (SST in D2 is colder than M2) corresponds to a more positive heat flux difference (net heat flux in D2 is larger). This is apparently a feedback of the atmospheric boundary layer to the SST changes such that a colder SST gives rise to a reduced (latent and sensible) heat flux (or increased net heat flux into the ocean). Therefore, the increased heat flux induced by transient winds is partially canceled by the SST-induced flux changes.

5. Summary

High-frequency (daily to intraseasonal) air–sea interaction processes are inadequately treated or resolved in most climate modeling studies. How this might affect the seasonal to interannual scale climate variability remains to be understood. In this study, we attempt to partially address this issue by examining the impacts of daily and monthly surface forcing in the upper ocean over the tropical Pacific using a reduced-gravity quasi-isopycnal ocean general circulation model (Schopf and Loughe 1995). The daily and monthly mean surface forcing are mostly direct estimates from observations for the 5-yr period from 1989 to 1993 except latent and sensible heat fluxes that are estimated through a diagnostic atmospheric boundary layer model (Seager et al. 1995). Two groups of experiments (each consists of one daily and one monthly forcing experiment) are carried out with the vertical base diffusion specified at 10−5 m2 s−1 and 5 × 10−6 m2 s−1, respectively, for the two groups.

The comparison between the simulations and observations reveals that the model SSTs have similar seasonal to interannual variability as the observed SST. But the magnitude of SSTs between the model and observations are quite different. In the western equatorial Pacific, model SSTs differ from the observed SST within ±1°C. In the eastern equatorial Pacific, SST differences between simulations and observations are also within ±1°C in each spring, but model SSTs are persistently warmer than the observed SST in each fall. This reflects the fact that the equatorial zonal gradient of SSTs in the model is weaker than for the observed. This systematic bias is not directly related to the existence or lack of higher-frequency atmospheric forcing, though it is possibly due to the systematic errors of the surface winds and the model vertical mixing parameterization.

The SST difference between the daily and monthly mean forcing experiments is generally negative, that is, SSTs in the daily experiments are colder than those in the monthly experiments. The difference develops mostly in the first year. Therefore, the cumulated difference of equatorial mixed layer heat budget between the daily and monthly forcing experiments for the first year is examined to identify the dominant mechanism. The negative SST difference in the western Pacific is primarily caused by enhanced latent heat loss due to the transient winds. In the eastern Pacific, all terms appear important. But our analysis reveals that all budget terms except zonal horizontal advection (ZADV) nearly balance out such that ZADV contributes to the cumulated difference of the mixed layer heat budget between the daily and monthly mean forcing experiments. Our analysis indicates that the enhanced mixing causes a smaller horizontal SST gradient and weaker vertical wind shear. The former causes a weaker thermal advection, and the latter causes weaker instability waves that also lead to weaker thermal advection.

In our experiments, we found that the transient winds directly produce enhanced latent and sensible heat loss and a deeper mixed layer. Both tend to lower SST. But colder SST can reduce latent and sensible heat fluxes that partially cancel the direct effect of the transient winds. In addition to decreasing surface layer temperature, the strong vertical mixing also causes variations in the thermocline depth so that the temperature near the thermocline is warmer in the daily experiment than in the monthly experiments.

A scale separation of the contributions to the mean heat budget of the mixed layer indicates that higher-frequency signals affect the horizontal heat convergence near the equator significantly, possibly through the instability waves in the central and eastern Pacific. The time mean entrainment, however, dominates the vertical transport. Sensitivity tests reveal that the daily surface forcing amplifies the model sensitivity to the parameterized diffusive entrainment into the mixed layer.

Acknowledgments

This research was supported by NASA TRMM and Seasonal to Interannual Prediction Projects. Ms. Anna Borovikov kindly provided her code for the computation of the mixed layer heat budget. Discussions with Dr. Paul Schopf and Dr. Bohua Huang greatly enhanced our understanding of the model results. Dr. Bohua Huang also read the manuscript and offered some valuable suggestions for improving the text.

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Fig. 1.
Fig. 1.

Time mean horizontal distributions of the net heat flux (W m−2) into the ocean for (a) the first-year mean simulation from D2, and (b) the ECMWF+UMD data averaged over the year 1989

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

Fig. 2.
Fig. 2.

Horizontal distributions of simulated SST (°C) of (a) D2 and (b) the NCEP SST averaged for the period of Nov 1992–Feb 1993

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

Fig. 3.
Fig. 3.

Time evolution of zonal distributions of net surface heat flux into the ocean along the equator from 1989 to 1993 (a) in D2 and (b) from ECMWF+UMD data. Contour interval is 25 W m−2 with areas of negative and large positive values (>125 W m−2) shaded

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

Fig. 4.
Fig. 4.

Time–zonal distributions of SST along the equator for (a) simulation D2 and (b) the NCEP data. Contour interval is 0.5°C with areas of SST > 29° and SST < 25°C shaded

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

Fig. 5.
Fig. 5.

Time series of SST (°C) average within (a) 150°–170°E and (b) 100°–120°W along the equator simulated in the four experiments and observed values (NCEP data)

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

Fig. 6.
Fig. 6.

Time series of (a) surface net heat flux (W m−2) into the ocean and (b) solar radiative flux (W m−2) during TOGA COARE (Nov 1992–Feb 1993) period. The solid line denotes D2 and the dashed line presents the observation at IMET buoy

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

Fig. 7.
Fig. 7.

Time series of (a) SST (°C) and (b) mixed layer depth (m) during TOGA COARE (Nov 1992–Feb 1993) period. The solid line denotes D2 and the dashed line presents the observation at IMET buoy

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

Fig. 8.
Fig. 8.

Time mean xz cross section of (a) temperature, (b) salinity, and (c) zonal current, along the equator during 1992–93 in D2. Contour intervals are 1°C, 0.1 psu, and 0.1 m s−1, respectively, with negative zonal current shaded

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

Fig. 9.
Fig. 9.

As in Fig. 8 except for D2−M2. Contour intervals are 0.2°C, 0.1 psu, and 0.05 m s−1, respectively, with negative values shaded

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

Fig. 10.
Fig. 10.

Separation of the total heat budgets into daily anomalies, seasonal to interannual anomalies, and time mean component within each 5° lat and 20° lon box along the equator during 1992–93 in D2

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

Fig. 11.
Fig. 11.

Time series of (a) mixed layer heat budgets for M2 and (b),(c) cumulative difference for D2−M2 averaged in the box (2.5°S–2.5°N, 150°–170°E). Unit is °C day−1

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

Fig. 12.
Fig. 12.

As in Fig. 11 except averaged in the box (2.5°S–2.5°N, 100°–120°W)

Citation: Journal of Climate 16, 4; 10.1175/1520-0442(2003)016<0756:TRODSF>2.0.CO;2

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